Erik B. Sudderth

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Alan S. WillskyProfessor of EECS, MITVerified email at mit.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Emily B. FoxStanford UniversityVerified email at stanford.edu
William T. FreemanProfessor of Computer Science, MITVerified email at mit.edu
Michael C. HughesAssistant Professor of Computer Science, Tufts UniversityVerified email at michaelchughes.com
Deqing SunResearch Scientist, GoogleVerified email at google.com
Antonio TorralbaProfessor of Computer Science, MITVerified email at csail.mit.edu
Michael J. BlackMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Zhile RenAppleVerified email at apple.com
Alexander IhlerUniversity of California, IrvineVerified email at ics.uci.edu
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyVerified email at berkeley.edu
Stuart RussellProfessor of Computer Science, University of California, BerkeleyVerified email at cs.berkeley.edu
Soumya GhoshMIT-IBM Watson AI Lab, IBM ResearchVerified email at cs.brown.edu
Hanspeter PfisterAn Wang Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
Michael I MandelAssociate Professor of Computer and Information Science at Brooklyn College, CUNYVerified email at sci.brooklyn.cuny.edu
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
Jan KautzVice President of Research, NVIDIA ResearchVerified email at nvidia.com
Michael IsardResearch Scientist, GoogleVerified email at google.com
Jyri J. KivinenLapland University of Applied SciencesVerified email at lapinamk.fi
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu

Erik B. Sudderth

Professor of Computer Science, UC Irvine

Verified email at uci.edu - Homepage

Machine Learning Computer Vision Statistics Bayesian Nonparametrics Signal Processing


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Nonparametric belief propagation EB Sudderth, AT Ihler, WT Freeman, AS Willsky IEEE Conference on Computer Vision & Pattern Recognition, 605-612, 2003	586*	2003
A sticky HDP-HMM with application to speaker diarization EB Fox, EB Sudderth, MI Jordan, AS Willsky The Annals of Applied Statistics, 1020-1056, 2011	532*	2011
Learning hierarchical models of scenes, objects, and parts EB Sudderth, A Torralba, WT Freeman, AS Willsky Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 2 …, 2005	428	2005
An HDP-HMM for systems with state persistence EB Fox, EB Sudderth, MI Jordan, AS Willsky Proceedings of the 25th international conference on Machine learning, 312-319, 2008	340	2008
Nonparametric belief propagation EB Sudderth, AT Ihler, M Isard, WT Freeman, AS Willsky Communications of the ACM 53 (10), 95-103, 2010	318	2010
Nonparametric Bayesian learning of switching linear dynamical systems E Fox, E Sudderth, M Jordan, A Willsky Advances in neural information processing systems 21, 2008	251	2008
Bayesian nonparametric inference of switching dynamic linear models E Fox, EB Sudderth, MI Jordan, AS Willsky IEEE Transactions on Signal Processing 59 (4), 1569-1585, 2011	250	2011
Visual hand tracking using nonparametric belief propagation EB Sudderth, MI Mandel, WT Freeman, AS Willsky 2004 Conference on Computer Vision and Pattern Recognition Workshop, 189-189, 2004	247	2004
Graphical models for visual object recognition and tracking EB Sudderth Massachusetts Institute of Technology, 2006	241	2006
Describing visual scenes using transformed objects and parts EB Sudderth, A Torralba, WT Freeman, AS Willsky International Journal of Computer Vision 77 (1), 291-330, 2008	240	2008
Shared segmentation of natural scenes using dependent Pitman-Yor processes E Sudderth, M Jordan Advances in neural information processing systems 21, 2008	224	2008
Describing visual scenes using transformed dirichlet processes A Torralba, A Willsky, E Sudderth, W Freeman Advances in neural information processing systems 18, 2005	186	2005
Sharing features among dynamical systems with beta processes E Fox, M Jordan, E Sudderth, A Willsky Advances in neural information processing systems 22, 2009	174	2009
Layered image motion with explicit occlusions, temporal consistency, and depth ordering D Sun, E Sudderth, M Black Advances in Neural Information Processing Systems 23, 2010	150	2010
Layered image motion with explicit occlusions, temporal consistency, and depth ordering D Sun, E Sudderth, M Black Advances in Neural Information Processing Systems 23, 2010	150	2010
Layered segmentation and optical flow estimation over time D Sun, EB Sudderth, MJ Black 2012 IEEE Conference on Computer Vision and Pattern Recognition, 1768-1775, 2012	145	2012
Three-dimensional object detection and layout prediction using clouds of oriented gradients Z Ren, EB Sudderth Proceedings of the IEEE conference on computer vision and pattern …, 2016	142	2016
Memoized online variational inference for Dirichlet process mixture models MC Hughes, E Sudderth Advances in neural information processing systems 26, 2013	115	2013
Joint modeling of multiple time series via the beta process with application to motion capture segmentation EB Fox, MC Hughes, EB Sudderth, MI Jordan The Annals of Applied Statistics 8 (3), 1281-1313, 2014	110	2014
Efficient multiscale sampling from products of Gaussian mixtures A Ihler, E Sudderth, W Freeman, A Willsky Advances in Neural Information Processing Systems 16, 2003	110	2003

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